Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound_Journal of Biomedical Engineering

Authors：

YANG Xuankai ¹ , SUN Jing ¹ , YANG Hongbo ^2,3 , GUO Tao ^2,3 , PAN Jiahua ^2,3 ,  WANG Weilian ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China;
2. Cardiovascular Hospital Affiliated to Kunming Medical University, Kunming 650102, P. R. China;
3. Fuwai Yunnan Cardiovascular Hospital, Kunming 650102, P. R. China;

Corresponding?author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

Heart sound; Congenital heart disease; Pulmonary arterial hypertension; High-frequency components; Statistical features; Extreme gradient boosting algorithm

DOI：

10.7507/1001-5515.202304037

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.

Citation： YANG Xuankai, SUN Jing, YANG Hongbo, GUO Tao, PAN Jiahua, WANG Weilian. Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound. Journal of Biomedical Engineering, 2024, 41(1): 41-50. doi: 10.7507/1001-5515.202304037 Copy

1.	Ruopp N F, Cockrill B A. Diagnosis and treatment of pulmonary arterial hypertension: a review. JAMA, 2022, 327(14): 1379-1391.
2.	Abman S H, Mullen M P, Sleeper L A, et al. Characterisation of paediatric pulmonary hypertensive vascular disease from the PPHNet Registry. European Respiratory Journal, 2022, 59(1): 2003337.
3.	Alnajjar M K, Abu-naser S S. Heart sounds analysis and classification for cardiovascular diseases diagnosis using deep learning. International Journal of Academic Engineering Research, 2022, 6(1): 7-23.
4.	Dwivedi A K, Ranjan H, Menon A, et al. Noise reduction in ECG signal using combined ensemble empirical mode decomposition method with stationary wavelet transform. Circuits, Systems, and Signal Processing, 2021, 40: 827-844.
5.	Simonneau G, Montani D, Celermajer D S, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. European Respiratory Journal, 2019, 53(1): 1801913.
6.	Chen Y, Wei S, Zhang Y. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Med Biol Eng Comput, 2020, 58(9): 2039-2047.
7.	Wang M, Wang J, Hu Y, et al. Detection of pulmonary hypertension with six training strategies based on deep learning technology. Computational Intelligence, 2022, 38(5): 1684-1706.
8.	Shukla S, Singh S K, Mitra D. An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. Biomedical Signal Processing and Control, 2020, 57: 101762.
9.	Kui Haoran, Pan Jiahua, Zong Rong, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomedical Signal Processing and Control, 2021, 69: 102893.
10.	Kaddoura T, Vadlamudi K, Kumar S, et al. Acoustic diagnosis of pulmonary hypertension: automated speech-recognition-inspired classification algorithm outperforms physicians. Scientific Reports, 2016, 6: 33182.
11.	Alqudah A M, Alquran H, Qasmieh I A. Classification of heart sound short records using bispectrum analysis approach images and deep learning. Network Modeling Analysis in Health Informatics and Bioinformatics, 2020, 9: 1-16.
12.	Ge B, Yang H, Ma P, et al. Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features. Biomedical Signal Processing and Control, 2023, 81: 104451.
13.	Juniati D, Khotimah C, Wardani D, et al. Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods//Proceedings of the Journal of Physics: Conference Series F, IOP Publishing, 2018, 953: 012202.
14.	Yildirim M. Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model. Concurrency and Computation: Practice and Experience, 2022, 34(24): e7232.
15.	Mahesh Kumar S V, Dhinakar P, Nishanth R. Aortic stenosis detection using spectral statistical features of heart sound signals. EAI Endorsed Transactions on Pervasive Health and Technology, 2022, 8(4): e5.
16.	何沛光, 宋偉, 吳杰, 等. 基于分割的自適應特征提取診斷心音方法. 計算機科學與應用, 2021, 11(8): 2051-2063.
17.	Xu W, Yu K, Ye J, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artificial Intelligence in Medicine, 2022, 126: 102257.
18.	Lahmiri S, Bekiros S. Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur. Chaos, Solitons & Fractals, 2022, 154: 111610.
19.	陳是扦, 彭志科, 周鵬. 信號分解及其在機械故障診斷中的應用研究綜述. 機械工程學報, 2020, 56(17): 91-107.
20.	周克良, 劉亞亞. 新閾值小波變換的心音去噪. 計算機工程與設計, 2020, 41(9): 2476-2481.
21.	陳強強, 成建波, 張剛, 等. 短時間序列的復雜程度分析方法研究. 艦船電子工程, 2022, 42(4): 145-150.
22.	楊洋, 郭興明, 鄭伊能, 等. 基于ICEEMDAN-MSE的左室舒張功能障礙心音信號的識別研究. 儀器儀表學報, 2022, 43(1): 274-281.
23.	劉美君, 吳全玉, 丁勝, 等. 自適應噪聲完備經驗模態分解排列熵結合支持向量機的心音分類方法研究. 生物醫學工程學雜志, 2022, 39(2): 311-319.
24.	李亞茹, 張宇來, 王佳晨. 面向超參數估計的貝葉斯優化方法綜述. 計算機科學, 2022, 49(S1): 86-92.

1. Ruopp N F, Cockrill B A. Diagnosis and treatment of pulmonary arterial hypertension: a review. JAMA, 2022, 327(14): 1379-1391.
2. Abman S H, Mullen M P, Sleeper L A, et al. Characterisation of paediatric pulmonary hypertensive vascular disease from the PPHNet Registry. European Respiratory Journal, 2022, 59(1): 2003337.
3. Alnajjar M K, Abu-naser S S. Heart sounds analysis and classification for cardiovascular diseases diagnosis using deep learning. International Journal of Academic Engineering Research, 2022, 6(1): 7-23.
4. Dwivedi A K, Ranjan H, Menon A, et al. Noise reduction in ECG signal using combined ensemble empirical mode decomposition method with stationary wavelet transform. Circuits, Systems, and Signal Processing, 2021, 40: 827-844.
5. Simonneau G, Montani D, Celermajer D S, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. European Respiratory Journal, 2019, 53(1): 1801913.
6. Chen Y, Wei S, Zhang Y. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Med Biol Eng Comput, 2020, 58(9): 2039-2047.
7. Wang M, Wang J, Hu Y, et al. Detection of pulmonary hypertension with six training strategies based on deep learning technology. Computational Intelligence, 2022, 38(5): 1684-1706.
8. Shukla S, Singh S K, Mitra D. An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. Biomedical Signal Processing and Control, 2020, 57: 101762.
9. Kui Haoran, Pan Jiahua, Zong Rong, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomedical Signal Processing and Control, 2021, 69: 102893.
10. Kaddoura T, Vadlamudi K, Kumar S, et al. Acoustic diagnosis of pulmonary hypertension: automated speech-recognition-inspired classification algorithm outperforms physicians. Scientific Reports, 2016, 6: 33182.
11. Alqudah A M, Alquran H, Qasmieh I A. Classification of heart sound short records using bispectrum analysis approach images and deep learning. Network Modeling Analysis in Health Informatics and Bioinformatics, 2020, 9: 1-16.
12. Ge B, Yang H, Ma P, et al. Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features. Biomedical Signal Processing and Control, 2023, 81: 104451.
13. Juniati D, Khotimah C, Wardani D, et al. Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods//Proceedings of the Journal of Physics: Conference Series F, IOP Publishing, 2018, 953: 012202.
14. Yildirim M. Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model. Concurrency and Computation: Practice and Experience, 2022, 34(24): e7232.
15. Mahesh Kumar S V, Dhinakar P, Nishanth R. Aortic stenosis detection using spectral statistical features of heart sound signals. EAI Endorsed Transactions on Pervasive Health and Technology, 2022, 8(4): e5.
16. 何沛光, 宋偉, 吳杰, 等. 基于分割的自適應特征提取診斷心音方法. 計算機科學與應用, 2021, 11(8): 2051-2063.
17. Xu W, Yu K, Ye J, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artificial Intelligence in Medicine, 2022, 126: 102257.
18. Lahmiri S, Bekiros S. Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur. Chaos, Solitons & Fractals, 2022, 154: 111610.
19. 陳是扦, 彭志科, 周鵬. 信號分解及其在機械故障診斷中的應用研究綜述. 機械工程學報, 2020, 56(17): 91-107.
20. 周克良, 劉亞亞. 新閾值小波變換的心音去噪. 計算機工程與設計, 2020, 41(9): 2476-2481.
21. 陳強強, 成建波, 張剛, 等. 短時間序列的復雜程度分析方法研究. 艦船電子工程, 2022, 42(4): 145-150.
22. 楊洋, 郭興明, 鄭伊能, 等. 基于ICEEMDAN-MSE的左室舒張功能障礙心音信號的識別研究. 儀器儀表學報, 2022, 43(1): 274-281.
23. 劉美君, 吳全玉, 丁勝, 等. 自適應噪聲完備經驗模態分解排列熵結合支持向量機的心音分類方法研究. 生物醫學工程學雜志, 2022, 39(2): 311-319.
24. 李亞茹, 張宇來, 王佳晨. 面向超參數估計的貝葉斯優化方法綜述. 計算機科學, 2022, 49(S1): 86-92.

Journal of Biomedical Engineering

Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content